An Improved ESPRIT Based Time-of-Arrival Estimation Algorithm for Vehicular OFDM Systems

TLDR: This paper proposes to improve upon the classical ESPRIT algorithm by incorporating the iterative reduced rank Hankel approximation (RRHA) technique, and shows that the degree of improvement brought forth increases with the increased number of RRHA-iterations.

Abstract: The exact time-of-arrival (TOA) information of the multi-path signals is crucial for optimal channel estimation in vehicular OFDM systems. Super-resolution algorithms such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) have been applied to retrieve this information from embedded pilots in the received OFDM symbols. These algorithms have high applicability for vehicular wireless environs characterized by high Doppler frequencies which leads to faster auto-correlation averaging. In this paper, we propose to improve upon the classical ESPRIT algorithm by incorporating the iterative reduced rank Hankel approximation (RRHA) technique. We show that this allows achieving of a lower MSE (mean square error) compared to the classical ESPRIT algorithm. Further, we portray that the degree of improvement brought forth increases with the increased number of RRHA-iterations.